Ecological risk assessment and early warning of heavy metal cumulation in the soils near the Luanchuan molybdenum polymetallic mine concentration area,Henan Province,central China

The Luanchuan molybdenum polymetallic mine concentration area is rich in mineral resources and has a long history of mining.The environmental impact of long-term mining activities cannot be ignored.It is of great significance to study the ecological risk and the accumulation trends of heavy metals in the soil of mining areas for scientific prevention and control of heavy metal pollution.Taking the Taowanbeigou River Basin in the mine concentration area as the research object,the ecological pollution risk and cumulative effect of heavy metals in the soil of the basin were studied by using the comprehensive pollution index method,potential ecological risk assessment method and geoaccumulation index method.On this basis,the cumulative exceeding years of specific heavy metals were predicted by using the early warning model.The comprehensive potential ecological risk of heavy metals in the soil near the Luanchuan mine concentration area is moderate,and the single element Cd is the main ecological risk factor,with a contribution rate of 53.6%.The overall cumulative degrees of Cu and Pb in the soil are“none-moderate”,Zn and Cd are moderate,Mo has reached an extremely strong cumulative level,Hg,As and Cr risks are not obvious,and the overall cumulative risks order is Mo>Cd>Zn>Cu>Pb>Hg.According to the current accumulation rate and taking the risk screening values for soil contamination of agricultural land as the reference standard,the locations over standard rates of Cu,Zn and Cd will exceed 78%in 90years,and the over standard rate of Pb will reach approximately 57%in 200 years.The cumulative exceeding standard periods of As,Cr and Hg are generally long,which basically indicates that these elements do not pose a significant potential threat to the ecological environment.Mining activities will accelerate the accumulation of heavy metals in soil.With the continuous development of mining activities,the potential pollution risk of heavy metals in the soil of mining areas will also increase.

Pattern changes and early risk warning of Spartina alterniflora invasion:a study of mangrove-dominated wetlands in northeastern Fujian,China

The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly spread wildly across coastal wetlands,challenging resource managers for control of its further spread.An investigation of S.alterniflora invasion and associated ecological risk is urgent in China's coastal wetlands.In this study,an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework.Predictions were made of'warning degrees':zero warning and light,moderate,strong,and extreme warning,by developing a back propagation(BP)artificial neural network model for coastal wetlands in eastern Fujian Province.Our results suggest that S.alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions.An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas.Areas of light warning and extreme warning accounted for43%and 7%,respectively,suggesting the BP neural network model is reliable prediction of the ecological risk of S.alterniflora invasion.The model predicts that distribution pattern of this invasive species will change little in the next 10 years.However,the invaded patches will become relatively more concentrated without warning predicted.We suggest that human factors such as land use activities may partially determine changes in warning degree.Our results emphasize that an early warning system for S.alterniflora invasion in China's eastern coastal wetlands is significant,and comprehensive control measures are needed,particularly for K.candel beach.

Integrated water risk early warning framework of the semi-arid transitional zone based on the water environmental carrying capacity (WECC)

Water risk early warning systems based on the water environmental carrying capacity(WECC)are powerful and effective tools to guarantee the sustainability of rivers.Existing work on the early warning of WECC has mainly concerned the comprehensive evaluation of the status quo and lacked a quantitative prejudgement and warning of future overload.In addition,existing quantitative methods for short-term early warning have rarely focused on the integrated change trends of the early warning indicators.Given the periodicity of the socioeconomic system,however,the water environmental system also follows a trend of cyclical fluctuations.Thus,it is meaningful to monitor and use this periodicity for the early warning of the WECC.In this study,we first adopted and improved the prosperity index method to develop an integrated water risk early warning framework.We also constructed a forecast model to qualitatively and quantitatively prejudge and warn about the development trends of the water environmental system.We selected the North Canal Basin(an essential connection among the Beijing-Tianjin-Hebei region)in China as a case study and predicted the WECC in 25 water environmental management units of the basin in 2018–2023.We found that the analysis of the prosperity index was helpful in predicting the WECC,to some extent.The result demonstrated that the early warning system provided reliable prediction(root mean square error of 0.0651 and mean absolute error of 0.1418),and the calculation results of the comprehensive early warning index(CEWI)conformed to the actual situation and related research in the river basin.From 2008 to 2023,the WECC of most water environmental management units in the basin had improved but with some spatial differences:the CEWI was generally poor in areas with many human disturbances,while it was relatively good in the upstream regions with higher forest and grass covers as well as in the downstream areas with larger water volume.Finally,through a sensitivity analysis of the indicators,we proposed specific management measures for the sustainability of the water environmental system in the North Canal Basin.Overall,the integrated water risk early warning framework could provide an appropriate method for the water environmental administration department to predict the WECC of the basin in the future.This framework could also assist in implementing corresponding management measures in advance,especially for the performance evaluation and the arrangement of key short-term tasks in the River Chief System in China.

Study on Positioning System of the Risk Early Warning for Enterprise Overseas Investment

From the perspective of internal and external environment analysis,we construct the risk identification index system for overseas investment enterprises.Combined with the theory of comprehensive evaluation and risk early warning,the risk location system of overseas investment is established.The risk intelligence decision model is constructed by rough set theory,and the risk identification,risk location and risk decision of overseas investment are studied,and are empirically analyzed with cases in overseas investment.

Early Warning of Commercial Housing Market Based on Bagging-GWO-SVM

A number of risks exist in commercial housing,and it is critical for the government,the real estate industry,and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early warning.In this paper,we examine the commodity housing market and construct a risk index for the commodity housing market at three levels:market level,the real estate industry and the national economy.Using the Bootstrap aggregating-grey wolf optimizer-support vector machine(Bagging-GWO-SVM)model after synthesizing the risk index by applying the CRITIC objective weighting method,the commercial housing market can be monitored for risks and early warnings.Based on the empirical study,the following conclusions have been drawn:(1)The commodity housing market risk index accurately reflect the actual risk situation in Tianjin;(2)Based on comparisons with other models,the Bagging-GWO-SVM model provides higher accuracy in early warning.A final set of suggestions is presented based on the empirical study.

An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells

Sticking is the most serious cause of failure in complex drilling operations.In the present work a novel“early warning”method based on an artificial intelligence algorithm is proposed to overcome some of the known pro-blems associated with existing sticking-identification technologies.The method is tested against a practical case study(Southern Sichuan shale gas drilling operations).It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state;furthermore,the results from four groups of verification samples are also consistent with the actual downhole state.This shows that the pro-posed training-based model can effectively be applied to practical situations.

Effect of an inpatient nursing risk early warning and control system in Shanghai:A retrospective study of adverse events

Purpose:To avoid the nursing risk of inpatients,reduce the occurrence of nursing errors and improve the safety of inpatients.Methods:We established a nursing risk early warning and control system,which includes a safety supervisory network,risk screening and early warning tools,and a risk control process.Results:The qualified rates of risk control measures to prevent pressure ulcers,unplanned extubation and fall/fall from bed all increased.The incidence of reported nursing errors decreased.The number of mistakes in medication-giving decreased.Conclusion:The establishment of an inpatient early warning and control system could effectively avoid nursing risk,improve risk prevention abilities,improve patient safety,and improve nursing quality.

Designing of Commercial Bank Loans Risk Early Warning System Based on BP Neural Networks

According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loaning corporation. Except the structure description of the system structure the demonstration of attemptive designing is also elaborated.

Study of Enterprises Marketing Risk Early Warning System Based on BP Neural Network Model

For effectively early warning the marketing risk caused along with the varied environment, a BP neural network method was introduced on the basis of analyzing the shortcomings of the risk early warning method, and combined with the practical conditions of dairy enterprises, the index system caused by the marketing risk was also studied. The＇principal component method was used for screening the indexes, the grades and critical values of the marketing risk were determined. Through the configuration of BP network, node processing and error analysis, the early warning resuits of the marketing risk were obtained. The results indicate that BP neural network method can be effectively applied through the function approach in the marketing early warning with incomplete information and complex varied conditions.

Risk Early-Warning Method for Natural Disasters Based on Integration of Entropy and DEA Model

Risk early-warning of natural disasters is a very intricate non-deterministic prediction, and it was difficult to resolve the conflicts and incompatibility of the risk structure. Risk early-warning factors of natural disasters were differentiated into essential attributes and external characters, and its workflow mode was established on risk early-warning structure with integrated Entropy and DEA model, whose steps were put forward. On the basis of standard risk early-warning DEA model of natural disasters, weight coefficient of risk early-warning factors was determined with Information Entropy method, which improved standard risk early-warning DEA model with non-Archimedean infinitesimal, and established risk early-warning preference DEA model based on integrated entropy weight and DEA Model. Finally, model was applied into landslide risk early-warning case in earthquake-damaged emergency process on slope engineering, which exemplified the outcome could reflect more risk information than the method of standard DEA model, and reflected the rationality, feasibility, and impersonality, revealing its better ability on comprehensive safety and structure risk.

Risk monitoring and early-warning technology of coal mine production

This article was written according to the secudty information theory and the secudty cybernetics basic principle, for reducing the accident risk effectively and safeguarding the production safety in coal mine. First, each kind of risk characteristic has carried on the earnest analysis to the coal-mining production process. Then it proposed entire wrap technology system of the risk management and the risk monitoring early warning in the coal-mining production process, and developed the application software-coal mine risk monitoring and the early warning system which runs on the local area network. The coal-mining production risk monitoring and early warning technology system includes risk information gathering, risk identification and management, risk information transmission; saving and analysis, early warning prompt of accident risk, safety dynamic monitoring, and safety control countermeasure and so on. The article specifies implementation method and step of this technology system, and introduces application situations in cooperating mine enterprise, e.g. Xiezhuang coal mine. It may supply the risk management and the accident prevention work of each kind of mine reference.

Oral microbiome and risk of malignant esophageal lesions in a high-risk area of China:A nested case-control study

Objective:We aimed to prospectively evaluate the association of oral microbiome with malignant esophageal lesions and its predictive potential as a biomarker of risk.Methods:We conducted a case-control study nested within a population-based cohort with up to 8 visits of oral swab collection for each subject over an 11-year period in a high-risk area for esophageal cancer in China.The oral microbiome was evaluated with 16 S ribosomal RNA(rRNA)gene sequencing in 428 pre-diagnostic oral specimens from 84 cases with esophageal lesions of severe squamous dysplasia and above(SDA)and 168 matched healthy controls.DESeq analysis was performed to identify taxa of differential abundance.Differential oral species together with subject characteristics were evaluated for their potential in predicting SDA risk by constructing conditional logistic regression models.Results:A total of 125 taxa including 37 named species showed significantly different abundance between SDA cases and controls(all P<0.05&false discovery rate-adjusted Q<0.10).A multivariate logistic model including 11 SDA lesion-related species and family history of esophageal cancer provided an area under the receiver operating characteristic curve(AUC)of 0.89(95%CI,0.84-0.93).Cross-validation and sensitivity analysis,excluding cases diagnosed within 1 year of collection of the baseline specimen and their matched controls,or restriction to screenendoscopic-detected or clinically diagnosed case-control triads,or using only bacterial data measured at the baseline,yielded AUCs>0.84.Conclusions:The oral microbiome may play an etiological and predictive role in esophageal cancer,and it holds promise as a non-invasive early warning biomarker for risk stratification for esophageal cancer screening programs.

Review of big-data and AI application in typhoon-related disaster risk early warning in Typhoon Committee region

ESCAP/WMO Typhoon Committee Members are directly or indirectly affected by typhoons every year.Members have accumulated rich experiences dealing with typhoons'negative impact and developed the technologies and measures on typhoon-related disaster risk forecasting and early warning in various ways to reduce the damage caused by typhoon.However,it is still facing many difficulties and challenges to accurately forecast the occurrence of typhoons and warning the potential impacts in an early stage due to the continuously changing weather conditions.With the development of information technology(IT)and computing science,and increasing accumulated hydro-meteorological data in recent decades,scientists,researchers and operationers keep trying to improve forecasting models based on the application of big data and artificial intelligent(AI)technology to promote the capacity of typhoon-related disaster risk forecasting and early warning.This paper reviewed the current status of application of big data and AI technology in the aspect of typhoon-related disaster risk forecasting and early warning,and discussed the challenges and limitations that must be addressed to effectively harness the power of big data and AI technology application in typhoon-related disaster risk reduction in the future.

联合国全民早期预警目标下中国气候风险管理前景分析

全球气候变暖对自然生态系统和人类社会产生了广泛而深远的影响,特别是加剧了气候变化风险,未来这种风险将更加复杂且难以管理。早期预警是减轻灾害风险和适应气候变化的重要手段。文章系统回顾了国内外早期预警发展及早期预警技术的全球演变历程,归纳总结了国际早期预警从概念提出到多灾种早期预警系统发展的4个阶段,剖析了气候变化背景下联合国全民早期预警倡议的3方面内涵。结合我国早期预警服务的现状和发展模式,以及新形势下面临的气候风险、预警需求和技术差距,提出了在联合国全民早期预警目标下,我国在气候风险管理方面的3个优先方向。一是加强对复合型极端天气气候事件发生发展机理和预报预警的科学研究,以及人类社会经济系统中潜在临界要素的研究,评估极端事件和临界要素引爆后的潜在级联影响和规模,提高灾害风险认知能力,拓深、拓广已有多灾种早期预警系统,提升防范新型气候灾害风险能力;二是识别气候变化对行业、领域和区域绿色低碳转型可能造成的产业风险,加快构建面向气候和气候变化尺度的国家级气候安全早期预警平台,提升全社会应对气候风险的能力;三是开展多灾种早期预警国际合作,帮助尚无早期预警系统或早期预警系统效力不足的国家建立和完善早期预警系统,重点是厘清区域和国家存在的主要气候灾害风险和等级,以及适应气候变化的紧迫需求,提供多品类气象防灾减灾公共产品,提高国际影响力。

早期预警评分联合SBAR沟通模式在高危新生儿中的应用

目的探讨改良早期预警评分(modified early warning score,MEWS)联合SBAR[现状(situation)、背景(background)、评估(assessment)、建议(recommendation)]沟通模式在高危新生儿中的应用效果,为临床提供一种有效评估患儿病情变化的沟通方法。方法采用前-后对照研究方法,选取2022年8月至9月入住本院新生儿科病房的高危新生儿270例作为研究对象。以8月入院的高危新生儿为对照组,9月入院的高危新生儿作为试验组,每组分别纳入135例患儿。对照组患儿按照常规护理实施病情观察,试验组患儿在对照组基础采用MEWS联合SBAR沟通模式实施病情观察。比较两组高危新生儿预警事件发生情况,护士预警事件与医生处理事件的一致率,医生对护士工作的满意率。结果两组均完成研究。对照组中63.6%的预警事件是由护士发现,试验组中92.6%的预警事件是由护士发现,两组比较,差异具有统计学意义(χ2=16.622,P<0.001)。试验组护士预警事件与医生处理事件的一致性(Kappa系数=0.926)高于对照组(Kappa系数=0.641);试验组医生对护士在专科知识、抢救应急能力、掌握病情情况、及时观察病情变化、医护配合、工作积极性、沟通能力、心理素质方面的满意率均高于对照组(80.0%~95.0%v30.0%~55.0%),两组比较,差异具有统计学意义(均P<0.05)。结论MEWS与SBAR沟通模式联合应用,有助于护士准确评估患儿病情变化,及时有效完成医护沟通,提高护士观察、沟通和处理能力,也提高了医生对护士工作的满意度。

基于风险评估模型的传染病预警体系的建立

目的建立一套基于风险评估模型的传染病预警体系,推动传染病管理水平的提升。方法通过搜集、整理最新的研究成果,参考相关的传染病预警评价指标体系的理论框架,构建基于风险评估模型的传染病预警体系。结果本次研究共纳入了12名专家,筛选出了三个一级指标,涉及流行前期指标、典型症状期指标、非典型症状期指标等,同时涉及7个二级指标。体系的必要性平均得分为8.27±0.24分,可获得性平均得分为7.74±0.34分。结论基于风险评估模型的传染病预警体系的建立有利于当地及时掌握传染病流行趋势,并进行调查指标分布趋势分析,然后提出相应的预警方案,可为有效预防传染病提供相关的依据。

基于PS-InSAR技术的晋城矿区地表形变监测及地质灾害风险预警

地表形变是一种严重的地质灾害现象,不仅严重影响灾害区居民的日常生活,而且会造成巨大的社会经济危害,尤其在采煤区。针对传统地表沉陷监测方法费时费力、无法获取地表沉降面状信息、难以进行地表沉陷灾害评估的不足,基于高分辨率SAR卫星影像,利用永久散射体合成孔径雷达干涉测量(PS-InSAR)技术对山西省晋城市晋城矿区2018年1月至2018年12月期间地表沉陷进行监测,分析获取了该地区地表连续形变情况,并利用该技术获取的海量PS点建立支持向量机(SVM)地质灾害风险评估预警模型,对晋城矿区周边居民点地质灾害风险进行了识别和预测。结果表明:晋城矿区10个煤矿及其周边区域存在较大的地表形变;晋城矿区平均LOS向年平均地表形变速率范围为-37~30.3 mm/a;PS-InSAR技术在晋城矿区地表形变监测中具有可行性,且可以实现矿区地质灾害风险综合识别和预警。

福建省贝类毒素风险预警与管控的现状与建议

贝类毒素主要由有毒赤潮藻产生,在海洋环境中分布广泛,它可通过食物链传递,对民众生命健康造成严重威胁。福建省近岸海域是我国赤潮高发海域之一,近年来在漳州、泉州等地曾发生民众食用贝类中毒事件。为保障民众健康和生命安全,维护海洋渔业经济健康发展,本文对福建省贝类毒素风险监测与管控、赤潮灾害预警与应急处置预案等相关政策、措施进行总结,结合近年来福建省海水贝类贝毒污染状况,对存在的主要问题进行分析,并针对性地提出健全相关法律法规、制定贝毒风险预警与应急处置预案、完善贝类毒素监测方案、严格产毒藻预警浓度、建立贝类毒素分级管控制度等对策和建议,以期为赤潮减灾、水产品质量安全管理,以及贝类毒素风险预警与管控体系的完善提供参考。

冬小麦农业气象灾害风险预警的业务实践与思考

开展冬小麦农业气象灾害风险预警工作是当前保障小麦生产安全和防灾减损的重要工作之一。2022年开始气象部门和农业部门联合开展了冬小麦农业气象灾害风险预警的业务服务工作,取得了一些技术成果和成功案例,但也存在一些问题和不足。本文简述了农业气象灾害风险预警业务的内涵,提出了综合农业气象灾害可能性预报、危险性和脆弱性预估,构建基于风险矩阵的冬小麦农业气象灾害风险预警业务的技术框架,并以2022年小麦干热风风险预警工作为例,展示了冬小麦农业气象灾害风险预警工作的技术流程。冬小麦农业气象灾害风险预警工作刚刚起步,尚有很多工作不完善,包括如何与已有的灾害风险评估成果进行深入融合,开展定量化的灾害影响预报和预警指标率定等。此外,在提升冬小麦农业气象灾害风险预警的准确率和开展服务效益评估等方面,未来还需要做大量的研发工作。

基于概率模型的四川省降水诱发地质灾害风险预警方法研究

开展降水诱发地质灾害风险预警是降低地质灾害风险的重要手段之一。基于2008-2019年四川省地质灾害历史灾情数据,在地理分区基础上,以地形、岩土、植被、气候因子为区划评价因子,采用信息量模型,将四川省划分为7个地质灾害分区。以正态化处理后的地质灾害有效降水量为建模因子,采用高斯拟合方法,建立各分区降水诱发地质灾害概率模型,并进行地质灾害预警检验。结果表明:(1)四川省可划分为盆地及盆周山区低易发区、中等易发区、高易发区,西南山地次易发区、易发区,川西高原次易发区、易发区等7个地质灾害分区。(2)ROC(Receiver Operating Characteristic)曲线分析表明,盆地及盆周山区的地质灾害分区精度略低于西南山地区和川西高原区的分区精度。(3)各分区正态化处理后的有效降水量与地质灾害频率具有很好的高斯分布关系。(4)建立的降水诱发地质灾害概率模型的地质灾害预警成功率为72%,当日降水量≥10 mm时,地质灾害预警成功率为83%。